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    "# 个性化推荐算法综述\n",
    "\n",
    "## 一、前述\n",
    "\n",
    "### 信息过载\n",
    "\n",
    "当物品的数量远超人可以手动遍历的范围，我们称之为信息过载。\n",
    "\n",
    "## 二、推荐系统\n",
    "\n",
    "### 什么是推荐系统\n",
    "\n",
    "没有明确需求的用户访问我们的服务，且该服务的物品对于用户而言构成了信息过载，系统通过一定的策略规则对物品进行了排序，并将排在前面的物品展示给用户，这样的系统就可以称之为推荐系统。（同时当用户有明确需求是即为搜索系统。）\n",
    "\n",
    "这些策略规则就可以称之为个性化推荐算法。\n",
    "\n",
    "### 个性化推荐算法起的作用\n",
    "\n",
    "在应用场景（电商、信息流、lbs）中用户面临的是数以万计的物品（商品、文章、餐馆等），而用户进入系统后往往面对的只是几个十几个物品，推荐算法如果精准的话可以提高用户在服务平台留存。所以，推荐算法在推荐系统中作用十分重要。\n",
    "\n",
    "### 如何衡量算法起到的作用\n",
    "\n",
    "- 线下：precise（精准率），recall（召回率）等。\n",
    "\n",
    "- 线上：业务指标，比如信息流中的CPR（平均阅读时长）、GMV（成交总额）、CR（转化率）。\n",
    "\n",
    "## 三、算法介绍\n",
    "\n",
    "### Recall（召回算法）\n",
    "\n",
    "- 基于用户展现点击的矩阵的分解算法\n",
    "\n",
    "- 基于图的推荐\n",
    "\n",
    "- 基于用户画像和商户画像\n",
    "\n",
    "- 基于深度学习的隐语义召回\n",
    "\n",
    "### Rank（排序算法）\n",
    "\n",
    "- LR（逻辑回归）\n",
    "\n",
    "- Gbdt（梯度提升决策树）\n",
    "\n",
    "- LR + Gdbt\n",
    "\n",
    "- DNN（wide and deep）\n",
    "\n",
    "## 四、评估算法\n",
    "\n",
    "#### 线下评估\n",
    "\n",
    "基于模型本身的指标\n",
    "\n",
    "- AUC\n",
    "\n",
    "- Recall\n",
    "\n",
    "- Precise\n",
    "\n",
    "- **Re Rank**\n",
    "\n",
    "- **Re Recom**\n",
    "\n",
    "#### 线上评估\n",
    "\n",
    "点击率、阅读时长、库内物品下发情况。\n",
    "\n",
    "## 五、前置知识\n",
    "\n",
    "python、词向量\n",
    "\n",
    "高数、线代、概率论\n",
    "\n",
    "机器学习基础、数据挖掘相关知识"
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